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Generalized iterative learning control with mixed system constraints: A gantry robot based verification

Generalized iterative learning control with mixed system constraints: A gantry robot based verification
Generalized iterative learning control with mixed system constraints: A gantry robot based verification
Iterative learning control (ILC) aims at improving the tracking performance of repetitive tasks based on information learnt from past attempts (trials). Modern practical applications demand more flexibility than current frameworks can deliver, in both how the task is specified and how system constraints are applied. To provide these features, an ILC framework is formulated in this paper for a generalized design objective with mixed system constraints, which includes intermediate position and sub-interval tracking as special cases. This is the first framework to combine a generalized ILC task description with constraint handling for continuous time systems. The successive projection method is applied to yield a comprehensive ILC algorithm with attractive convergence properties and computationally efficient implementation. This algorithm is verified experimentally on a gantry robot test platform, whose results reveal its practical efficacy and robustness against model uncertainty.
0967-0661
Chen, Yiyang
da753778-ba38-4f95-ad29-b78ff9b12b05
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Liu, Yanhong
c4b4a3da-3e3b-4cd0-8d54-2c3e40cfa4ea
Chen, Yiyang
da753778-ba38-4f95-ad29-b78ff9b12b05
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Freeman, Christopher T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Liu, Yanhong
c4b4a3da-3e3b-4cd0-8d54-2c3e40cfa4ea

Chen, Yiyang, Chu, Bing, Freeman, Christopher T. and Liu, Yanhong (2020) Generalized iterative learning control with mixed system constraints: A gantry robot based verification. Control Engineering Practice, 95, [104260]. (doi:10.1016/j.conengprac.2019.104260).

Record type: Article

Abstract

Iterative learning control (ILC) aims at improving the tracking performance of repetitive tasks based on information learnt from past attempts (trials). Modern practical applications demand more flexibility than current frameworks can deliver, in both how the task is specified and how system constraints are applied. To provide these features, an ILC framework is formulated in this paper for a generalized design objective with mixed system constraints, which includes intermediate position and sub-interval tracking as special cases. This is the first framework to combine a generalized ILC task description with constraint handling for continuous time systems. The successive projection method is applied to yield a comprehensive ILC algorithm with attractive convergence properties and computationally efficient implementation. This algorithm is verified experimentally on a gantry robot test platform, whose results reveal its practical efficacy and robustness against model uncertainty.

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More information

Accepted/In Press date: 25 November 2019
e-pub ahead of print date: 4 December 2019
Published date: February 2020

Identifiers

Local EPrints ID: 436883
URI: http://eprints.soton.ac.uk/id/eprint/436883
ISSN: 0967-0661
PURE UUID: ae0afa5e-1145-4aa4-9185-76f87d1fbccb
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 13 Jan 2020 17:31
Last modified: 07 Oct 2020 02:01

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